Social and Behavioral Sciences Commons^™

“Regression To The Mean”: The Confluence Of Eugenics And Statistics In The 19th And 20th Centuries, Emrys G. King

Pomona Senior Theses

The work of this thesis is twofold — first, qualitatively characterizing the confluence between the British eugenics and statistics movements in the late 19th and early 20th centuries, and second, quantitatively analyzing the effect of this foundation on pedagogical materials in the growing field of statistics between 1880 and 1970. Towards the first goal, the history of the method of least squares, state statistics, and positive and negative eugenics are outlined, followed by a close reading of the foundational texts authored by Francis Galton and Karl Pearson that introduced linear regression. Towards the latter goal, English-language statistics textbooks published between …

Sealing The Deal: A Case Study Of A Private, Southeastern, Regional College’S Student Onboarding Practices, Alicia R. Gaston, Kristen Meyer, Tyler Ogden, Jennifer Perkinson, Casey Yocum Michaels

Doctor of Education Capstones

Each year, higher education institutions offer support to new, incoming students through an onboarding process that requires dedication and collaboration across multiple departments. Offering streamlined guidance and clear communication throughout the onboarding process is essential to ensure incoming students understand action steps without becoming overwhelmed with new terminology, processes, and environments. This explanatory case study is set to understand the current communication practices and technology use across onboarding departments at Brightside College – also referred to as Brightside or BC (pseudonym). The research team aimed to understand Brightside's onboarding staff's perspective of current processes and practices. With emphasis on the …

Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for …

Cyberattacks On Port Infrastructures: A Decade Of Trends, Incidents, And Mitigation Strategies (2011-2024), Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Port infrastructures are critical to global trade, handling over 80% of the world's cargo by volume. However, their increasing reliance on digital technologies has exposed them to a wide range of cyber threats. This paper provides a comprehensive analysis of cyberattacks targeting port infrastructures from 2011 to the present. We examine the types of attacks, geographical distribution, notable incidents, and underlying vulnerabilities. Additionally, we discuss mitigation strategies and future directions for enhancing cybersecurity in the maritime sector. Our findings highlight the urgent need for robust regulatory frameworks, advanced technological solutions, and collaborative efforts to safeguard critical port operations.

Enhancing Adhd Diagnosis In College Students Using Multimodal Integration Of Nicats And Iva-2 Tools, Rushmila Shabneen

College of Graduate Studies: Theses & Dissertations

This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with multimodal metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS), which monitors facial expressions, eye movements, and computer interactions. Preliminary results show that combining these tools improves ADHD detection, reduces false …

Integrating Satellite-Based Precipitation Analysis: A Case Study In Norfolk, Virginia, Imiya M. Chathuranika, Dalya Ismael

Engineering Technology Faculty Publications

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then …

Essays On Technological Change And Scientific Research, Max Mayca

Electronic Theses & Dissertations (2024 - present)

The primary objective of economic science has long been to understand and promote growth, as it contributes to a more prosperous civilization and improves individual well-being. The core drivers of growth—capital accumulation, human capital development, and technological progress—are well-established. While capital deepening enables scaling of production, and human capital enhances labor productivity, technological and scientific advancements remain the most critical, as they continually redefine the efficiency frontier. Given this centrality, it is essential to understand how technological progress affects labor markets, how research resources should be allocated, and how technology relates to human capital formation.

The first part of this …

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Neural Correlates Of Attentional Biases In Dietary Choice: Role Of Childhood Socioeconomic Status, Justine Jamie N. Gotico

CMC Senior Theses

Childhood poverty has been shown to increase adult risk for obesity above and beyond its direct effects on adult socioeconomic status (SES). One proposed mechanism of these effects is by shifting behavioral patterns of dietary consumption and choice, for example by increasing rapid attention to high-calorie unhealthy foods. Yet, whether such neural mechanisms can explain observed differences in dietary behavior based on childhood SES remains an open question. Here we used event-related potentials (ERPs) to examine early attentional correlates of low childhood SES during a dietary choice task, based on research suggesting that early attentional biases toward high-calorie foods emerge …

Consequences Of Artificial Intelligence In Health Insurance: Lawsuits, Policy, And Ethics, Alyssa N. Roberts

Honors Undergraduate Theses

In recent years, the healthcare system has been burdened by a multitude of obstacles that hinder the ability to provide effective, affordable, and timely care. Among these, one of the most significant challenges is the role that health insurance plays in shaping the quality of care. Health insurance companies are designed to decrease financial strain on patients, but they have introduced inefficiencies through delayed coverage approvals, increased denials, and administrative costs. Artificial intelligence (AI) has started to play an integral role in resolving these issues for the health insurance industry. Through its quick automated claim processing, fraud screening, and reduced …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

Match Accuracy Of Burned Teeth: A Pilot Study Of Allied Dental Professionals, Brenda T. Bradshaw, Marsha A. Voelker, Samantha C. Vest, Sinjini Sikdar

Dental Hygiene Faculty Publications

Purpose: The purpose of this pilot study was to assess allied dental professionals' match accuracy of burned teeth; a skill required by disaster victim identification (DVI) team members.

Methods: This cross-sectional study used a convenience sample of registered dental hygienists (RDH) (n=15) and dental assistants (DA) (n=15) to assess their match accuracy of burned teeth with simulated antemortem (AM) and postmortem (PM) images. Fifteen human teeth were heated at 400°C for 15 minutes. Prior to and following heat alteration, each tooth was photographed and radiographed. Images were presented to participants in randomized order, and they were instructed to correctly match …

Relational Database Schema To Support Research Profiling Studies, Natural Language Processing, And Bibliometric Analysis, Darnelle Melvin

Library Faculty Research

In this paper, a relational database schema is introduced that supports rapid prototyping, data preprocessing, and warehousing tasks associated with research profiling studies, natural language processing, and bibliometric analysis. Python scripts are leveraged for the seamless retrieval and processing of data from Semantic Scholar. This schema is tailored to efficiently analyze entities such as authors, their scientific papers, referenced papers, and cited papers. Adhering to the relational model, this schema offers a standardized approach to data storage and detailed information retrieval for scientific papers. Enhancing knowledge discovery in scientific databases, this schema provides researchers with a powerful platform for robust …

Covariance Matrix Forecasting Of Equity Portfolios, Michael Nebor

Graduate Research Theses & Dissertations

This dissertation consists of two papers. The first paper introduces DCC-SVR, a hybrid Dynamic Conditional Correlation (DCC) and Support Vector Regression (SVR) method of forecasting the covariance matrix. This paper shows that DCC-SVR is able to outperform the traditional methods of DCC and rolling historical on multiple data sets. Performance is shown for both standard GARCH and GJR-GARCH methods. This paper also analyzes performance when dimensions are increased to 49 dimensions and when an application using equal weighted portfolio allocation is used.

The second paper introduces a covariance matrix forecasting method based on copula-GARCH simulated returns. The accuracy of this …

Efficient Algorithms For Nearest Correlation Matrix Computation With Missing Data, Ibrahim Eniola Oyeyinka

Graduate Research Theses & Dissertations

This thesis investigates efficient algorithms for computing the Nearest Correlation Matrix (NCM) under incomplete financial data. Correlation matrices are vital in portfolio optimization and risk management, yet empirical estimates often violate symmetry, positive semidefiniteness, and unit diagonal conditions due to missing observations. Two projection-based methods are analyzed: the Modified Alternating Projections (MAP) and Anderson Acceleration (AA). Theoretical analysis using convex optimization and normal cone characterization supports numerical evaluation on synthetic and real-world stock-return matrices (550×550, 2020–2025). Missing data are modeled through Missing Completely at Random (MCAR) and Not Missing at Random (NMAR) mechanisms. The results show that AA converges faster …

A Modern Optimization Approach With Data-Driven Analytical Modeling For The Healthcare Business Segment (Hbs) From The S&P 500, Aditya Chakraborty, Chris Tsokos

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Introduction: The S&P consists of eleven business segments, which are classified according to the type of industry. The current study focuses on developing a non-linear analytical model for the Healthcare Business Segment (HBS) of the S&P 500, as a function of different economic & financial indicators. Materials and Methods: The analytical model used six financial indicators together with four economic indicators to predict the weekly average closing price (WCP) of HBS stocks. Johnson’s SB transformation corrected skewness, while desirability-based optimization identified indicator values maximizing WCP. The model’s performance and generalizability were validated through repeated 10-fold cross-validation. Results: All attributable contributors …

Cyber Warfare And The Future Of Conflict, Victor A. Mercado

Graduate Theses/Dissertations

This thesis examines whether cyber warfare now poses a more immediate threat to U.S. national security than weapons of mass destruction (WMD). Cyber operations have become a dominant instrument of contemporary conflict, with malicious actors operating in a persistent “grey zone” that blurs traditional boundaries between war and peace. These operations target military and civilian entities, both directly and often as collateral damage due to the uncontrollable nature of cyber threats. WMDs, despite their catastrophic destructive potential, remain largely constrained by established deterrence frameworks.

The evolving nature of cyber warfare warrants comparison to WMD effects and impact, as cyber capabilities …

Fostering Critically Conscious Lesson Planning In A Generative Artificial Intelligence Era, Derek Riddle, Paula Cristina Azevedo, Catharyn Shelton, Jaime Colwell, Jori Beck

Teaching & Learning Faculty Publications

Teacher candidates (TCs) use digital resources and social media to plan and develop learning material, and with publically accessible generative artificial intelligence (GAI), TCs are able to generate lesson plans within seconds rather than hours or days. While there is research on how to support TCs' evaluation of reliable digital media, there is little known on how to prepare TCs for GAI content. Using the complementary frameworks of Freire’s (1970) critical consciousness and Jonnasen’s (1991) theory of constructivism, this in-progress design based research seeks to develop an adaptable framework that addresses the evolving nature of technology, specifically GAI, and the …

Exploring Physics Teacher Identity: Pathways Toward Equitable Instruction Among Teachers, Clausell Mathis, Jomo Mutegi, Turhan Carroll, Maya Patel, Andrea Wooley

Teaching & Learning Faculty Publications

Physics teacher identity encompasses teachers' beliefs and views toward being a physics teacher. Physics teacher identity is influenced by teachers' perspectives on physics teaching and learning. Previous studies on teacher identity in general suggest that the construct is complex and malleable. Studies further suggest that it influences instructional methods, student engagement, and classroom environment. However, within physics education, there has yet to be an examination of how physics teacher identity influences "equitable" teaching approaches. This qualitative study sought to identify various dimensions of physics teacher identity and its implications for physics education research. We interviewed secondary and postsecondary physics teachers …

Meteorology, Weather And War In South East Asia: Malaya C. 1940-1960, Fiona Williamson

Research Collection College of Integrative Studies

This article interrogates the positioning of British colonial meteorology in Malaysia and Singapore from the 1940s to 1960. This period spanned a global conflict and an internecine war, effecting profound sociopolitical changes from which neither Malaysia nor Singapore would emerge the same. The meteorological services were essential to Britain's armed conflicts, providing vital weather information to the army, navy and, especially, the air forces, as well as supporting the aviation and shipping industry often in difficult and dangerous circumstances. This article argues that British military policy in South East Asia and the specific concerns of the colonial government in Malaya …

Risk Spillover Effect Of China-Asean Supply Chains: Insights Of Industrial Transfer, Zeyang Bian, Yuning Zhang, Keng Siau, Yaqian Zhang, Jianjia He

Research Collection School Of Computing and Information Systems

As labour costs in China increase, labour-intensive industries are migrating to ASEAN countries, attracted by lower labour costs and market potential. This shift not only affects the economies of China and ASEAN but also reshapes the global manufacturing landscape. This paper investigates the correlation and spillover of supply chain risks using production exposure indicators derived from inter-country input-output data and the R-Vine Copula model. We assess the risk spillover of each country within the global supply chain. Our findings indicate that industrial relocation can significantly alter supply chain structures, thereby affecting the concentration and direction of risks. While China's role …

Review Of Digital Degrowth: Technology In The Age Of Survival, Michael Kirby

Publications and Research

No abstract provided.

The Limits Of Technocratic Degrowth: A Review Of The End Of Capitalism: Why Growth And Climate Protection Are Incompatible—And How We Will Live In The Future, Michael Kirby

Publications and Research

This book review discusses Ulrike Herrmann’s The End of Capitalism: Why Growth and Climate Protection Are Incompatible—and How We Will Live in the Future, situating it within contemporary climate debates. Herrmann’s central claim—that capitalism’s dependence on perpetual growth renders meaningful climate protection impossible—is presented as a clear, accessible synthesis of degrowth arguments. The book is particularly notable for its rejection of techno-utopian solutions such as carbon capture and its skepticism vis-à-vis large-scale renewable transitions under market conditions. However, the review ultimately questions the feasibility of Herrmann's proposed alternative (the “survival economy”), demonstrating that it overlooks political power, class interests, and …

A Comparative Analysis Of Hedging And Safe-Haven Properties Of Cryptocurrencies And Commodities, Tari M. Karimo, Ochoche Abraham

CBN Journal of Applied Statistics (JAS)

No abstract provided.

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Analyzing Visual Attention In Virtual Crime Scene Investigations Using Eye-Tracking And Vr: Insights For Cognitive Modeling, Wen-Chao Yang, Chih-Hung Shih, Jiajun Jiang, Sergio Pallas Enguita, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A 360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated …

Copyright And Artificial Intelligence, Part 2: Copyrightability

Copyright, Fair Use, Scholarly Communication, etc.

This report by the United States Copyright Office addresses the legal and policy issues related to artificial intelligence (AI) and copyright as outlined in the Office’s August 2023 Notice of Inquiry (NOI).

The report will be published in several parts each one addressing a different topic. This part addresses the copyrightability of works created using generative AI. The first part, published in 2024, addresses the topic of digital replicas—the use of digital technology to realistically replicate an individual’s voice or appearance. A subsequent part will turn to the training of AI models on copyrighted works, licensing considerations, and allocation of …

Empowering Crisis Information Extraction Through Actionability Event Schemata And Domain-Adaptive Pre-Training, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5% and 10.1% in micro and macro F1 …

Maximum Trimmed Likelihood Estimation For Discrete Multivariate Vasicek Processes, Thomas M. Fullerton Jr., Michael Pokojovy, Andrews T. Anum, Ebenezer Nkum

Mathematics & Statistics Faculty Publications

The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of …

Including Individuals' Sense Of Self In Digital Information Privacy, Peter N. Meso, Solomon Negash, Humayun Zafar, Gurpreet Dhillon

Faculty Articles

The nature of contemporary digital ecosystems causes concerns that affect the person, the individual-self, an integral part of an individual’s information privacy calculus and hence a component of individuals’ Information Privacy Concerns (IPC). Yet, prior IPC models overlook self-focused concerns. This study articulates two constructs, termed “loss of autonomy” (i.e., autonomy) and “control over profiling” (i.e., profiling), that reflect individuals’ self-focused privacy concerns. Combining these new constructs with conventional IPC constructs that capture data-focused and device-focused concerns yields an IPC model made up of three dimensions: self-focused concerns, data-focused concerns, and device-focused concerns. The authors first develop instrument items for …